---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
datasets:
- reddit_tifu
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: Bart_reddit_tifu
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: reddit_tifu
      type: reddit_tifu
      config: long
      split: train
      args: long
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.2709
    - name: Precision
      type: precision
      value: 0.8768
    - name: Recall
      type: recall
      value: 0.8648
    - name: F1
      type: f1
      value: 0.8705
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Bart_reddit_tifu

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the reddit_tifu dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5035
- Rouge1: 0.2709
- Rouge2: 0.0948
- Rougel: 0.2244
- Rougelsum: 0.2244
- Gen Len: 19.3555
- Precision: 0.8768
- Recall: 0.8648
- F1: 0.8705

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| 2.6968        | 1.0   | 2370 | 2.5385          | 0.2634 | 0.0907 | 0.218  | 0.2182    | 19.4438 | 0.8766    | 0.8641 | 0.8701 |
| 2.4746        | 2.0   | 4741 | 2.5077          | 0.273  | 0.0941 | 0.2238 | 0.2239    | 19.2572 | 0.8774    | 0.8655 | 0.8712 |
| 2.3066        | 3.0   | 7111 | 2.5012          | 0.2671 | 0.0936 | 0.221  | 0.2211    | 19.3071 | 0.8756    | 0.864  | 0.8696 |
| 2.2041        | 4.0   | 9480 | 2.5035          | 0.2709 | 0.0948 | 0.2244 | 0.2244    | 19.3555 | 0.8768    | 0.8648 | 0.8705 |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0